Machine learning techniques to predict the effectiveness of music therapy: A randomized controlled trial. (March 2020)

• Record Type: Journal Article
• Authors: Raglio, Alfredo; Imbriani, Marcello; Imbriani, Chiara; Baiardi, Paola; Manzoni, Sara; Gianotti, Marta; Castelli, Mauro; Vanneschi, Leonardo; Vico, Francisco; Manzoni, Luca
• Is Part Of: Computer methods and programs in biomedicine. Volume 185(2020)
• Publication Date: 2020-03
• Subjects: Therapeutic music listening -- Medicine -- Machine learning techniques -- Decision tree methods -- Therapeutic predictivity; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.105160
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Music listening is widely used in clinical settings to modulate arousal levels. Which and how music can be selected in therapeutic treatments are unclear points. Decision trees enable finding therapeutic predictivity factors of music interventions in healthy participants. Machine learning techniques can support music therapy practice. Abstract: Background: The literature shows the effectiveness of music listening, but which factors and what types of music produce therapeutic effects, as well as how music therapists can select music, remain unclear. Here, we present a study to establish the main predictive factors of music listening's relaxation effects using machine learning methods. Methods: Three hundred and twenty healthy participants were evenly distributed by age, education level, presence of musical training, and sex. Each of them listened to music for nine minutes (either to their preferred music or to algorithmically generated music). Relaxation levels were recorded using a visual analogue scale (VAS) before and after the listening experience. The participants were then divided into three classes: increase, decrease, or no change in relaxation. A decision tree was generated to predict the effect of music listening on relaxation. Results: A decision tree with an overall accuracy of 0.79 was produced. An analysis of the structure of the decision tree yielded some inferences as to the most important factors in predicting the effect of music listening, particularly the initial relaxation level, the combination of education and musical training, age, and music listening frequency. Conclusions: The resulting decision tree and analysis of this interpretable model makes it possible to find predictive factors that influence therapeutic music listening outcomes. The strong subjectivity of therapeutic music listening suggests the use of machine learning techniques as an important and innovative approach to supporting music therapy practice.